Applications of microbeam analytical techniques in gold deportment studies and characterization of losses during the gold recovery process
Bibliographic record
Abstract
Many gold deposits are characterized by the presence of refractory (submicroscopic) gold in the matrix of sulfide minerals which is not directly amenable to gold cyanidation. In order to recover this submicroscopic gold, the ore has to be oxidized before being subjected to gold cyanidation and extraction. This is done by autoclave pressure oxidation (AC POX), a technology commonly used in the mining industry for ores with a high refractory gold content. Gold ores commonly contain active carbonaceous materials which have the ability to adsorb, or preg‐rob, gold during the AC POX and/or cyanidation steps of the recovery process, and gold losses can be significant. Advanced microbeam analytical techniques such as dynamic secondary ion mass spectrometry (D‐SIMS) and time‐of‐flight secondary ion mass spectrometry (TOF‐SIMS) have become powerful tools for characterization of different forms and carriers of gold in the mining industry. Major advantages of these techniques are related to the investigation of individual mineral particles and quantitative analysis with detection limits in the low ppm/ppb concentrations. This paper describes various microbeam techniques and procedures implemented at Surface Science Western (SSW) which have become an intricate part of a comprehensive mineralogical and analytical approach for ore characterization and process mineralogy. Copyright © 2017 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".